Sr. Staff Engineer – Applied AI, Patient

We're hiring a Sr. Staff Engineer – Applied AI, Patient to help Patient Engineering build and scale AI/ML-powered patient experiences across areas such as matching, ranking, recommendations, onboarding, and personalization.

Rula is at an important stage in its ML journey. We already have ML models and pipelines powering product experiences, with additional capabilities under development. At the same time, much of the engineering foundation required to develop, deploy, evaluate, and operate ML consistently at scale is still ahead of us.

This is a deeply hands-on technical leadership role. You'll develop and improve production models and AI-powered systems, build critical capabilities needed by Patient Engineering, and partner closely with Rula's ML team on shared infrastructure, models, standards, and architectural decisions.

You won't own a single model or product surface. You'll go where the highest-leverage Applied AI problems are—developing models, building reusable capabilities, improving existing systems, or determining whether a problem is best solved with custom ML, a foundation model/AI service, deterministic software, or a hybrid approach.

Success means enabling Patient Engineering to build better intelligent experiences while creating a strong technical partnership with Rula's ML team—resulting in better models, reusable capabilities, and faster, higher-quality delivery of AI/ML-powered patient experiences.

Required Qualifications

• 10+ years of software and/or ML engineering experience, including significant experience designing, building, deploying, and operating production ML systems with measurable product or business impact. Particularly Python, with experience building scalable systems and personally writing and reviewing production-quality code.
• Deep hands-on ML expertise and technical leadership across the ML lifecycle: demonstrated experience with problem formulation, data/feature engineering, model development and training, evaluation, experimentation, deployment, monitoring, and continuous improvement, while setting technical direction across teams, influencing architecture, mentoring senior engineers, and exercising strong judgment about when to use custom ML, foundation models/AI services, deterministic approaches, or hybrids.
• Demonstrated 0→1 experience building shared ML infrastructure or reusable capabilities adopted across teams, such as feature infrastructure, training/inference pipelines, model serving, evaluation, observability, or lifecycle tooling.
• Deep experience with recommendation, ranking, relevance, personalization, search, or matching systems, including rigorous evaluation connecting offline model performance to online experiments and product/business outcomes.
• Strong evaluation and experimentation rigor connecting offline model performance to online experiments and product/business outcomes.
• Hands-on experience building and operating modern foundation model/GenAI systems in production, including evaluation, reliability, latency, cost, privacy/safety, and operational tradeoffs.

Preferred Qualifications

• Experience helping an organization evolve from early-stage or ad hoc ML development toward mature ML engineering practices.
• Experience operating ML systems in healthcare, financial services, or another regulated/high-trust environment.